What techniques to explore for dynamic clustering of documents (emails)?
It sounds like you are trying to do some kind of semi-supervised learning. In semi-supervised learning, some data points are labelled (you know which class they belong to), and others are not. There are classification algorithms designed specifically for this kind of problem, like a transductive-SVM. I personally have not found these techniques to be more effective than simply discarding the unlabelled data and treating my problem as purely supervised, but YMMV.

TFIDF remains fairly popular, as do ngram-based approaches. A more modern vectorization to consider might be word2vec, which translated something like a bag-of-words style vector into a more meaningful feature space for words.